Chan, TzeHaw and Lye, Chun Teck and Hooy, CheeWooi (2010): Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?

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Abstract
Being a small and open economy, the stability and predictability of Malaysian foreign exchange are crucially important. However, despite the general failure of conventional monetary models, foreign exchange misalignments and authority intervention have both caused the forecasting process an uneasy task. The present paper employs the monetaryportfolio balance exchange rate model and its modified version in the analysis. We then compare two Artificial Neural Networks (ANNs) estimation procedures (MLFN and GRNN) with random walk (RW) in the modelingprediction process of RM/USD during the postBretton Wood era (1990M12008M8). The outofsample forecasting assessment reveals that the ANNs have outperformed the RW, which in particular, the MLFNs outperform GRNNs where as the latter outperform the RW models with consistency in both the exchange rate models by all evaluation criteria. In addition, the findings also show that the modified model has superior forecasting performance than the first model. In brief, economic fundamentals are vital in forecasting and explaining the RM/USD exchange rate. The findings are beneficial in policy making, investment modeling as well as corporate planning.
Item Type:  MPRA Paper 

Original Title:  Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work? 
English Title:  Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work? 
Language:  English 
Keywords:  Artificial Neural Networks, Forecasting, modified monetaryportfolio balance model, RM/USD 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods; Simulation Methods C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics F  International Economics > F3  International Finance > F31  Foreign Exchange 
Item ID:  26326 
Depositing User:  TzeHaw Chan 
Date Deposited:  03. Nov 2010 08:30 
Last Modified:  11. Feb 2013 21:19 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/26326 